We are recording neural dynamics at high resolution in the brains of behaving mice with intact function and in mouse models of Parkinson’s. We are defining disturbed dynamics and their relationship to movement impairments, and testing brain stimulation approaches designed to steer disturbed dynamics towards normative regimes for behavioural gains.

We use a variety of motor tasks together with brain- and myoelectric-controlled interface paradigms to study the neural correlates of motor learning.

We research ways to boost motor learning and motor recovery after brain injury. This includes non-invasive brain stimulation during learning and during sleep. We also investigate the impact that sleep disruption has on consolidation of motor learning. 

We are broadly interested in the field of neuroscience, especially insofar as it addresses the questions motor learning. Using mathematical and computational tools, we model motor learning and adaptation. 

We use high-density electrophysiological recordings across forebrain circuits to define neural substrates of goal-directed movements. We develop closed-loop stimulation approaches to provide novel treatments for Parkinson’s disease and essential tremor.

We have a long-standing interest in uncovering the principles of neural function during movement and motor learning. For example, we have shown that the collective dynamics of neural populations are stable over time and across individuals performing the same task, and how these dynamics change during different forms of learning or across tasks.

We have two major research interests - to understand how brain dynamics change during motor skill acquisition, both in health and after brain injuries such as a stroke, and to develop new methods to modulate these dynamics to improve functional outcome.

Our prior work revealed the important role of beta reduction and post-movement beta rebound in the sensorimotor cortex and the STN for motor execution and learning, respectively. In Parkinson’s disease, impaired beta modulation disrupts these functions. We aim to use adaptive DBS to modulate beta oscillations to improve motor symptoms in PD.

We are working on predicting pathological subthalamic nucleus (STN) beta activity before its onset, in order to develop algorithms that deliver deep brain stimulation proactively in order to prevent pathological bursts. We are also leveraging DBS device accelerometer signals to track physiological states for predictive control. 

We investigate how cells and circuits in the brain work together to perform computations that support memory. We use a wide range of technical approaches to both record and perturb brain activity, with the aim of establishing a causal relationship between memory computations and core symptoms reported in people with psychiatric disorders.

We study how the brain maps experiences into hippocampal representations and rebalances these networks during learning and recall. Using neural recordings and behavioural analysis, we explore how memory maps are formed, reorganised, and updated to support flexible memory and behaviour.

We are broadly interested in the field of neuroscience, especially insofar as it addresses the questions of learning and memory. Using mathematical and computational tools, we model synaptic plasticity across different time scales that reproduces experimental findings.

We are particularly interested in triggering the brain to practice memories during sleep. This ‘brainwashing’ can strengthen target memories, helping the brain to make new connections and think creatively. It can also be used to make upsetting memories less bothersome and may eventually be used in treating Depression and PTSD.

We are using transcranial ultrasound (TUS) to enhance cholinergic outputs from the basal forebrain in Parkinson's disease dementia and dementia with Lewy bodies.  

We are using mouse models to gain new insights into how disturbed neural dynamics underlie the sleep problems that commonly emerge in Parkinson’s. We aim to test whether longitudinal brain-stimulation interventions can restore normative sleep-related neural dynamics and alter the trajectories of behavioural impairment and cellular pathology.

We investigates how cells and circuits in the sleeping brain perform computations that support memory. Using a wide range of technical approaches, we seek to establish new tools for enhancing these computations in the sleeping brain, to improve memory in the wakeful state.

We use wireless, wearable electronics to provide 24 hr/day recordings from multiple brain areas in freely-behaving non-human primates. We are interested in understanding conserved patterns of neuronal dynamics across wake and sleep. We also have a particular interest in studying cerebellar activity during sleep and consolidation.

We aim to understand how sleep changes with recovery from brain injury, using techniques to measure sleep in the rehabilitation hospital and home. We collaborate with other CoRE groups to develop and test closed-loop non-invasive brain stimulation during sleep, testing effects on slow wave sleep and consolidation of learning. 

We examines ways in which the sleeping brain processes information, and how this can be nudged in order to improve memory, enhance creativity, and improve mental wellbeing. We are also working on ways to slow down the aging process by artificially boosting sleep oscillations.
 

With the aim of improving sleep across brain disorders, we will use the closed-loop auditory and electrical stimulation to reinforce oscillatory activities that control forebrain activity during sleep. In addition, we will develop methods of delivering these interventions in specific sleep stages and points in the diurnal cycle.

Our recent work has identified pathological activities in the STN LFPs underlying specific symptoms of sleep disorders in PD. We will test the hypothesis that tailored aDBS protocols, designed to suppress pathological oscillations while preserving physiological rhythms during sleep, will improve the treatment of sleep disturbances in PD.

We have plans to deliver low frequency Deep Brain Stimulation (DBS) at night in patients with Parkinson's disease, in order to enhance memory and cognitive functions. 

We seeks to define LFP-based physiomarkers for computations that support memory, such as Sharp-Wave Ripples (SWRs), which provide a potential target for device-based neuromodulation. 

We study the role of neuronal oscillations in health and disease with a particular focus on closed-loop control of these oscillations for therapeutic benefits.

We study physiomarkers as neural signatures of cognitive states, focusing on electrophysiological signals linked to memory and disease. 

As an adjunct to clinical care, we are implanting fine electrodes into the thalami of children undergoing recordings of their brains during seizures. These recordings allow us to investigate the role of the thalamus in their seizures at a personalised level and to explore their optimal targets for neuromodulation therapy. 

Our previous work contributed to the identification of beta in STN LFPs as a physiomarker for bradykinesia and rigidity in PD. Recently, we showed that stimulation-induced resonant activity (ERNA) in STN LFPs reliably reflects arousal and sleep stages. We will study ERNA as a physiomarker for automated DBS programming and adaptive DBS feedback.

We are using sensing enabled Deep Brain Stimulation (DBS) devices, to identify neurophysiological biomarkers for optimising adaptive DBS. 

We have written and shared software for statistical analysis of functional Magnetic Resonance Imaging. Our group has written and shared spiking neural network models designed to simulate memory processing. 

We use a variety of implantable and wearable devices to implement closed-loop neurostimulation using optogenetics, electrical stimulation and auditory stimulation.

We develop tools including algorithms and software for analysing neural data, modelling brain dynamics, and decoding cognitive states. These tools support our research on memory and brain disorders, and aim to enable scalable, reproducible, and translational neuroscience.

We are a clinical and research group based at Great Ormond Street Hospital and the UCL-Great Ormond Street Institute of Child Health who are working together to improve surgical treatments and outcomes for children with epilepsy. We are exploring the use of novel deep brain stimulation devices to treat children with drug resistant epilepsy.

We develop software and hardware to deliver closed loop auditory, electrical and optogenetic stimulation across a range of applications in experimental animals and humans. 

We are developing novel tools to deliver closed-loop stimulation to the brain across a range of applications.

We proposed a hardware design that synchronizes sampling with stimulation pulses, preventing artifact capture while preserving continuous LFP recording. We aim to create next-generation bi-directional neural interfaces with superior artifact rejection, fast processing, and flexible control for testing advanced stimulation protocols in humans.

We are using sensing enabled Deep Brain Stimulation (DBS) devices, to identify neurophysiological biomarkers for optimising adaptive DBS. 

To understand where, when and how neural dynamics arise in the brain and underpin behaviour, we use in vivo electrophysiological and photometric recording techniques, behavioural phenotyping, and a range of approaches for manipulating nerve cells and circuits in mice, including in models capturing specific features of human brain conditions.

We use a wide range of technical approaches to guide discovery of computations that support memory in the mammalian brain. We use causal manipulations to perturb these computations, to help establish how core symptoms reported in people with psychiatric disorders arise from perturbations to neural circuit computation.

We conduct neurophysiological studies in humans, rodents and non-human primates, as well as in vitro studies using brain slices.

We use an empirical neuroscience approach by combining in vivo recordings, imaging, and behavioural experiments to study memory processes. We collect and analyse real neural data from behaving animals to uncover how brain circuits encode, store, and adapt memories in naturalistic conditions.

We make high-density electrophysiological recordings from behaving rodents. We use these recordings to define circuit-wide responses to novel-closed-loop stimulation, to define neural signatures of specific disease processes and to elucidate the neural substrates of underpinning motor control and cognition.

We record from large neural populations during various innate and trained behaviours in mice, and use behavioural and neural perturbations to isolate the unique contributions of different brain regions during motor learning, execution, and adaptation. We also perform complementary behavioural experiments and spinal motoneuron recordings in humans.

We use an empirical neuroscience approach by recording electrophysiological signals (LFPs, EEG, EMG and etc) from human patients and healthy participants during motor and cognitive tasks. This enables us to examine how neural oscillations support movement and cognition, and how these processes are disrupted in movement disorders.

Our research includes human participants, both healthy participants and people with brain injury such as stroke. 

We collaborate with several clinical teams in order to provide proof-of-principle evidence that closed-loop stimulation approaches, often developed in preclincal settings,  can alter neuronal activity and/or behavior in humans.

We work with people living with neurological disorders, particularly stroke survivors, to understand how the brain can recover motor function after an injury, and how we can develop novel approaches to improve these physiological mechanisms to improve recovery.  

We record electrophysiological signals from patients undergoing treatments such as dopaminergic medication and high-frequency DBS. This enables us to investigate how these interventions modulate neural oscillations and how such changes relate to symptom improvement, advancing our understanding of disease pathophysiology and treatment mechanisms.

Our research is primarily patient-facing and aims to elucidate the mechanistic links between neural dynamics and neurological symptoms, using these insights to inform device-based therapies.

We use modelling to gain mechanistic insight into computations that support memory. This ranges from using advanced statistical methods such as machine learning, to building proof-of-principal spiking neural network models.

We are interested in using computational models of neuronal dynamics to optimise algorithms for closed-loop control.

We are building models of brain networks using a range of imaging techniques including volumetry, diffusion MRI and resting fMRI. These models can be interrogated to provide prognostic information and therefore guide the choice of optimal patient and treatment modality.

We are broadly interested in the field of neuroscience, especially insofar as it addresses the questions of learning and memory. Using mathematical and computational tools, we model synaptic plasticity across different time scales that reproduces experimental findings. 

Our group uses computer simulations and mathematical analyses to understand learning and activity dynamics of brain networks, and how they are effected by brain stimulation. We use models to investigate how neural circuits learn and work in the healthy state, and how their dynamics and information processing may be best restored by treatments.

We are very interested in explanation in neuroscience, especially the "neural manifold view" of brain function, which posits that the collective dynamics of neural populations "do the doing" in the brain. We use models to test this notion, and also to explore hypotheses about motor learning and adaptation derived from experiments.

We use computational approaches including machine learning algorithms, to understand the links between neural activity at short timescales and behaviour. 

We develop a range of implantable and wearable neural interface technologies.

We develop novel devices to deliver closed-loop stimulation approaches in preclincal and clinical settings.

We often use using brain-computer interfaces to test hypotheses about neural function, and those insights along with our work on neural manifolds can be translated to neurotechnologies. As part of our human work, we are developing neural interfaces that allow people with motor disorders to control external devices using motoneuron activity.

We aim to create next-generation bi-directional neural interfaces with superior artifact rejection, fast processing, and flexible control for testing advanced stimulation protocols in humans. We also applying more advanced control algorithms, such as model predictive control, in aDBS.

Our methods rely heavily biomedical engineering approaches.